While lifelong learning attempts to address various shortcomings of modern AI, numerous lifelong learning algorithms pay special attention to catastrophic forgetting~, ). Catastrophic forgetting is the behavior of an algorithm or model where, after training the model on one task, training on a new task adversely effects model's performance on the first task; often to the point that the model performs worse than a model behaving randomly. Nuance between how to calculate relevance and minimization of adverse effects differentiates algorithms such as elastic weight consolidation (EWC,~), continual learning through synaptic intelligence (SI,~), and memory aware synapses (MAS,~[1]}). SI, for example, holds weights relatively fixed according to the sensitivity of loss function with respect to them, whereas MAS considers sensitivity of weights in the predictor itself. EWC, on the other hand, uses Fisher information for determining how to update weights. Gradient episodic memory (GEM,~) like SI stores a subset of previous task data and seeks to minimize cost function on current labeled data point while constraining that loss on the episodic memory does not increase.
